CN101982825B - Method and device for processing video image under intelligent transportation monitoring scene - Google Patents
Method and device for processing video image under intelligent transportation monitoring scene Download PDFInfo
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Abstract
The invention discloses a method for processing a video image under an intelligent transportation monitoring scene, which comprises the following steps: A. determining a lane detection region and determining the lane direction by analyzing the running track of a moving target in the lane detection region in continuous M frames of video images; B. respectively determining whether shadows exist in each later frame of the video image according to the lane direction and executing step C if determining that the shadows exist in continuous N frames of the video images; and C. respectively inhibiting the shadows in each later frame of the video image according to a hue, saturation and value (HSV) color model, determining whether the contact ratio of foreground images in each frame of the video image before and after the inhibition meets the requirements, and repeatedly executing step B if continuous L frames meet the requirements, wherein, M, N and L are positive integers which are greater than 1. The invention further discloses a device for processing a video image under the intelligent transportation monitoring scene. The method and the device of the invention can effectively inhibit the shadows in the video images.
Description
Technical field
The present invention relates to the intelligent traffic monitoring technology, particularly method of video image processing and the device under a kind of intelligent transportation monitoring scene.
Background technology
In the last few years, the infrastructure investment dynamics was increasing, and road construction is one of them.Because the road construction phase is generally longer, its growth rate does not catch up with the growth rate of vehicle far away, therefore so that the traffic in each city goes from bad to worse, and the vehicle supervision department in each city sets up the intelligent traffic monitoring system of perfect in shape and function, is the effective ways that improve present current situation of traffic.Specifically, namely by the video image of each road of camera acquisition, each frame video image that collects is analyzed, carried out wagon flow statistics etc. according to analysis result, and then realize traffic control and management.
Because the impact of illumination, when light source when incident direction is subject to occlusion, can produce shade at the opposite side of vehicle.Owing to often have identical motion feature between shade and the moving target (being vehicle), therefore follow-up when analyzing for video image, dash area may be incorporated into mistakenly and be moving target, thereby the shape of moving target is affected, as the shape that makes moving target becomes large, and then cause that a plurality of moving targets are sticked together etc., affect subsequent treatment.Therefore, need to suppress shade, but also not have a kind of effective inhibition method in the prior art.
Summary of the invention
In view of this, fundamental purpose of the present invention is to provide the method for video image processing under a kind of intelligent transportation monitoring scene, can effectively suppress the shade in the video image.
Another object of the present invention is to provide the video image processing device under a kind of intelligent transportation monitoring scene, can effectively suppress the shade in the video image.
For achieving the above object, technical scheme of the present invention is achieved in that
Method of video image processing under a kind of intelligent transportation monitoring scene comprises:
A, determine lane detection zone, and determine the track direction by the running orbit of analyzing the moving target in the lane detection zone in the continuous N frame video image;
B, according to described track direction, whether have shade in the every frame video image after determining respectively, all have shade, then execution in step C in the N continuous frame video image if determine;
C, according to hue, saturation, intensity HSV colour model, every frame video image is afterwards carried out respectively shade to be suppressed, and whether the registration of the foreground image in the every frame video image before and after determine suppressing reach requirement, if the L frame all reaches requirement continuously, and repeated execution of steps B then; Described M, N and L are the positive integer greater than 1.
Video image processing device under a kind of intelligent transportation monitoring scene comprises:
Track orientation determination module is used for determining the lane detection zone, and determines the track direction by the running orbit of analyzing the moving target in the lane detection zone in the continuous N frame video image, and the track direction of determining is sent to the shadow Detection module;
Described shadow Detection module is used for according to described track direction, whether has shade in the every frame video image after determining respectively, all has shade in the N continuous frame video image if determine, and then notifies shade to suppress module and carries out self function;
Described shade suppresses module, be used for according to hue, saturation, intensity HSV colour model, every frame video image is afterwards carried out respectively shade to be suppressed, and whether the registration of the foreground image in the every frame video image before and after determining to suppress reaches requirement, if the L frame all reaches requirement continuously, then notify described shadow Detection module to repeat self function; Described M, N and L are the positive integer greater than 1.
As seen, adopt technical scheme of the present invention, can effectively suppress the shade in the video image, thereby make things convenient for subsequent treatment; And scheme of the present invention implements simple and convenient, is convenient to popularize.
Description of drawings
Fig. 1 is the process flow diagram of the inventive method embodiment.
Fig. 2 is from the lower left to top-right track direction synoptic diagram among the inventive method embodiment.
Fig. 3 is from the lower right to upper left track direction synoptic diagram among the inventive method embodiment.
Fig. 4 is a detected target area synoptic diagram among the inventive method embodiment.
Fig. 5 be among the inventive method embodiment the target area is equally divided into about synoptic diagram after two sub regions.
Fig. 6 is the composition structural representation of apparatus of the present invention embodiment.
Embodiment
For problems of the prior art, the video image processing scheme under a kind of brand-new intelligent transportation monitoring scene is proposed among the present invention, use this scheme and can effectively suppress shade in the video image.
For make technical scheme of the present invention clearer, understand, referring to the accompanying drawing embodiment that develops simultaneously, scheme of the present invention is described in further detail.
Fig. 1 is the process flow diagram of the inventive method embodiment.As shown in Figure 1, may further comprise the steps:
Step 11: determine the lane detection zone, and determine the track direction by the running orbit of analyzing the moving target in the lane detection zone in the continuous N frame video image.
According to the installation situation of the video camera under the existing intelligent transportation monitoring scene, the track direction only have following three kinds may, that is: 1) from the lower left to the upper right side; 2) from the lower right to the upper left side; 3) under to directly over.
Fig. 2 is from the lower left to top-right track direction synoptic diagram among the inventive method embodiment.Fig. 3 is from the lower right to upper left track direction synoptic diagram among the inventive method embodiment.
In this step, at first determine the lane detection zone, this zone need to comprise the straight way part in track, and avoid comprising bend and turnout part as far as possible, shown in Fig. 2 and 3, white line is wherein extended to the edge of video image to two ends, the rectangular area that defines is the lane detection zone.For every video camera, in case after it installs, utilize the lane detection zone in its every frame video image that collects all identical.
Afterwards, can by the running orbit of the moving target in the lane detection zone continuous N frame (from the 1st frame to the M frame) video image is analyzed, determine the track direction.How the movement locus of moving target is analyzed and be prior art.M is the positive integer greater than 1, and span is generally 200～500.
Step 12: according to the track direction, whether have shade in the every frame video image after determining respectively, all have shade in the N continuous frame video image if determine, then execution in step 13.
In this step, at first according to the track direction, determine moving target type to be detected.For different track directions, wait difference owing to block, the shade of generation is also with difference.Such as, if the regulation vehicle is kept to the right, so when the track direction be during from the lower left to the upper right side, the more close video camera of upstroke target (vehicle that travels to the upper right side from the lower left) meeting, because the relation of projection angle, the shade that produces can be more obvious, therefore on this track direction, should more pay close attention to the upstroke target, be that moving target type to be detected is the upstroke target, when the track direction is during from the lower right to the upper left side, moving target type to be detected is the downward movement target; If the regulation vehicle is kept to the left, so when the track direction be during from the lower left to the upper right side, moving target type to be detected is the downward movement target, when the track direction is during from the lower right to the upper left side, moving target type to be detected is the upstroke target; Distinguishingly, when the track direction be under to directly over the time, moving target type to be detected both can be the upstroke target, also can be the downward movement target.
In addition, the textural characteristics of shade and the textural characteristics of moving target have larger difference, wherein, the texture of shade is more smooth a little less than can be, and the texture of moving target then can be abundanter, therefore, in this step, can determine whether there is shade in every frame video image based on textural characteristics.
Specifically, for every frame video image X, can come in the following manner respectively to determine wherein whether to exist shade:
1) detect the target area at the moving target place of each respective type in the lane detection zone from video image X, the target area refers to comprise the minimum rectangular area of moving target.
If moving target type to be detected is the downward movement target, so then in the lane detection zone of video image X, detect the target area at each downward movement target place.
According to existing detection mode, can include simultaneously moving target and shade (if hypographous words) thereof in the target area.Fig. 4 is a detected target area synoptic diagram among the inventive method embodiment.
2) each target area is equally divided into about two sub regions, calculate the average gradient amplitude of every sub regions
With
, and calculate
With
In smaller value and the ratio ρ of higher value.
Fig. 5 be among the inventive method embodiment the target area is equally divided into about synoptic diagram after two sub regions.
In addition, can utilize the Sobel operator to calculate the average gradient amplitude of every sub regions
With
, how to be calculated as prior art; Afterwards, calculate
With
In smaller value and the ratio ρ of higher value.
3) calculate the mean value of ρ corresponding to all target areas
For video image X, may detect a plurality of target areas, each target area all can be according to step 2) shown in mode calculate a ρ, in this step, calculate the mean value of ρ corresponding to all target areas
4) will
With the first threshold T that sets in advance
GCompare, if
Less than T
G, then determine to have shade among the video image X.
T
GSpan be generally 0.4～0.7.
All there is shade in the N continuous frame video image if determine in the manner described above, but execution in step 13 then.N is the positive integer greater than 1, and span is generally 500～2000.
Step 13: according to hue, saturation, intensity (HSV) colour model, every frame video image is afterwards carried out respectively shade to be suppressed, and whether the registration of the foreground image in the every frame video image before and after determining to suppress reaches requirement, if the L frame all reaches requirement continuously, then repeated execution of steps 12.
In the HSV colour model, brightness (V) component is used for the expression light action in the sensation of the caused bright degree of human eye, and it is relevant with the luminous intensity of object; Tone (H) component is used for the expression human eye and sees the color sensation that the light time of one or more wavelength produces, and the kind of its reflection color is the fundamental characteristics that determines color, such as the tone that just refers to such as red, brown; Saturation degree (S) component is used for the purity of expression color, the degree of namely mixing white light, and gradation of color in other words, for the identical colorama of tone, saturation degree more dark colour is distincter or say purer.Usually tone component and saturation degree component are referred to as chromatic component.
In addition, the camera acquisition normally brightness of video image of arriving, colourity (YUV) form.
In this step, for every frame video image Y, can come in such a way respectively that it is carried out shade and suppress:
1) each pixel among the traversal video image Y is found out luminance component less than the pixel of the luminance component of the corresponding pixel points in the background image that is similarly yuv format that obtains in advance.
Corresponding pixel points refers to the pixel that coordinate is identical.For instance, be the pixel of (1,1) for the coordinate among the video image Y, coordinate is that the pixel of (1,1) is its corresponding pixel points in the background image.How the background extraction image is prior art.
Suppose Lum
Curr(i) luminance component of a pixel among the expression video image Y, Lum
Bg(i) luminance component of its corresponding pixel points in the expression background image so then has: Lum
Bg(i)-Lum
Curr(i)＞T
LumT
LumGreater than 0, the luminance component difference between two pixels of the larger expression of value is larger, T
LumSpan be generally 10～60.
Because the characteristic of shade, wherein each pixel, namely the luminance component of shadow spots is usually less than the luminance component of corresponding pixel points in the background image, so in this step, will satisfy Lum
Bg(i)-Lum
Curr(i)＞T
LumPixel as doubtful shadow spots, and for these doubtful shadow spots, further carry out subsequent treatment.
2) video image Y and background image all are converted to the HSV form, and three parameters are set.
How to be converted to prior art.
3) for each the pixel Z that finds out, carry out respectively following processing:
3.1) luminance component of calculating pixel point Z in the HSV model and the ratio of the luminance component of corresponding pixel points in the HSV model, if this ratio is positioned at the interval (α, β) that sets in advance, then the value of first parameter is set to 1, otherwise, be set to 0; The saturation degree component of calculating pixel point Z in the HSV model and the difference of the saturation degree component of corresponding pixel points in the HSV model are if this difference is less than the Second Threshold T that sets in advance
s, then the value of second parameter is set to 1, otherwise, be set to 0; The tone component of calculating pixel point Z in the HSV model and the absolute value of the difference of the tone component of corresponding pixel points in the HSV model are if this absolute value is less than the 3rd threshold value T that sets in advance
H, then the value of the 3rd parameter is set to 1, otherwise, be set to 0.
Wherein, α represents that light is strong and weak, and the value of the stronger α of light is larger, but can not be excessive, otherwise can cause shade to suppress excessively; β represents the robustness to noise, and the larger adaptability to noise of value is stronger, but can not be excessive, otherwise can cause shade to suppress not enough; Usually, the span of α is that the span of 0.3～0.7, β is 0.9～1.0.
In addition, T
sSpan be generally 0.04～0.1; T
HSpan be generally 20～40.
3.2) multiply by respectively the weights that set in advance for it with the value of each parameter, and with the multiplied result addition, if addition result, determines then that pixel Z is shadow spots greater than the 4th threshold value that sets in advance, and with its filtering.
Because tone component, luminance component are different with the importance of saturation degree component, therefore need to be for it arranges different weights, wherein, the weight of luminance component is maximum, secondly is the saturation degree component, minimum be the tone component.Such as, the weight of tone component, saturation degree component, luminance component can be set to respectively 1,3,5 or 1,4,5.In addition, in actual applications, the weight of saturation degree component also can be identical with the weight of luminance component, such as, the weight of tone component, saturation degree component and luminance component can be set to respectively 1,5,5.
How the filtering pixel is prior art.
After finishing in the manner described above the shade inhibition to every frame video image, calculate the registration of the foreground image in the every frame video image before and after suppressing, how to be calculated as prior art, if shadow-free or shade are less in the video image before suppressing, the registration of the foreground image before and after suppressing so can be very high, therefore, the registration that calculates and the 5th threshold value that sets in advance can be compared, if registration is greater than the 5th threshold value, determine that then registration reaches requirement, and after determining continuous L frame video image and all reaching requirement, repeated execution of steps 12.L is the positive integer greater than 1, and span is generally 500～2000.
The concrete value of the 4th threshold value and the 5th threshold value all can be decided according to the actual requirements.
So far, namely finished introduction about the inventive method embodiment.
Based on above-mentioned introduction, Fig. 6 is the composition structural representation of apparatus of the present invention embodiment.As shown in Figure 6, comprising:
Track orientation determination module 61 is used for determining the lane detection zone, and determines the track direction by the running orbit of analyzing the moving target in the lane detection zone in the continuous N frame video image, and the track direction of determining is sent to shadow Detection module 62;
Shade suppresses module 63, be used for according to the HSV colour model, every frame video image is afterwards carried out respectively shade to be suppressed, and whether the registration of the foreground image in the every frame video image before and after determining to suppress reaches requirement, if the L frame all reaches requirement continuously, then notify shadow Detection module 62 to repeat self function; M, N and L are the positive integer greater than 1.
Wherein, can specifically comprise (for simplifying accompanying drawing, not shown) in the shadow Detection module 62:
The first processing unit, be used for according to the track direction, determine moving target type to be detected, described moving target type comprises upstroke target and downward movement target, and for every frame video image X, carry out respectively following processing: detect the target area at the moving target place of each respective type in the lane detection zone from video image X, the target area is the minimum rectangular area that comprises moving target; Two sub regions about each target area is equally divided into are calculated the average gradient amplitude of every sub regions
With
, and calculate
With
In smaller value and the ratio ρ of higher value; Calculate the mean value of ρ corresponding to all target areas
Will
Compare with the first threshold that sets in advance, if
Less than first threshold, then determine to have shade among the video image X;
The second processing unit is used for when the first processing unit is determined the N continuous frame video image and all had shade, and the notice shade suppresses module 63 execution self function.
Wherein, if the regulation vehicle is kept to the right, then when the track direction be during from the lower left to the upper right side, moving target type to be detected is the upstroke target, when the track direction is during from the lower right to the upper left side, moving target type to be detected is the downward movement target;
If the regulation vehicle is kept to the left, then when the track direction be during from the lower left to the upper right side, moving target type to be detected is the downward movement target, when the track direction is during from the lower right to the upper left side, moving target type to be detected is the upstroke target.
Shade suppresses can specifically comprise in the module 63 (for simplifying accompanying drawing, not shown):
The 3rd processing unit, be used for the video image Y for every frame yuv format, carry out respectively following processing: each pixel among the video image Y of traversal yuv format, find out luminance component less than the pixel of the luminance component of the corresponding pixel points in the background image of the yuv format that obtains in advance, corresponding pixel points is the identical pixel of coordinate; The video image Y of yuv format and the background image of yuv format all are converted to the HSV form, and three parameters are set; For each the pixel Z that finds out, carry out respectively following processing: the luminance component of calculating pixel point Z in the HSV model and the ratio of the luminance component of corresponding pixel points in the HSV model, if this ratio is positioned at the interval that sets in advance, then the value of first parameter is set to 1, otherwise, be set to 0, the saturation degree component of calculating pixel point Z in the HSV model and the difference of the saturation degree component of corresponding pixel points in the HSV model, if this difference is less than the Second Threshold that sets in advance, then the value of second parameter is set to 1, otherwise, be set to 0, the tone component of calculating pixel point Z in the HSV model and the absolute value of the difference of the tone component of corresponding pixel points in the HSV model, if this absolute value is less than the 3rd threshold value that sets in advance, then the value of the 3rd parameter is set to 1, otherwise, be set to 0; Value with each parameter multiply by respectively the weights that set in advance for it, and with the multiplied result addition, if addition result, determines then that pixel Z is shadow spots greater than the 4th threshold value that sets in advance, and with its filtering; Afterwards, calculate the registration of the foreground image in the every frame video image before and after suppressing, registration and the 5th threshold value that sets in advance are compared, if registration, determines then that registration reaches requirement greater than described the 5th threshold value;
The manages the unit everywhere, is used for notifying shadow Detection module 62 to repeat self function when the 3rd processing unit is determined continuous Y frame video image and all reached requirement.
The specific works flow process of device embodiment shown in Figure 6 please refer to the respective description in the embodiment of the method shown in Figure 1, repeats no more herein.
The above only is preferred embodiment of the present invention, and is in order to limit the present invention, within the spirit and principles in the present invention not all, any modification of making, is equal to replacement, improvement etc., all should be included within the scope of protection of the invention.
Claims (5)
1. the method for video image processing under the intelligent transportation monitoring scene is characterized in that, comprising:
A, determine lane detection zone, and determine the track direction by the running orbit of analyzing the moving target in the lane detection zone in the continuous N frame video image;
B, according to described track direction, whether have shade in the every frame video image after determining respectively, all have shade, then execution in step C in the N continuous frame video image if determine;
C, according to hue, saturation, intensity HSV colour model, every frame video image is afterwards carried out respectively shade to be suppressed, and whether the registration of the foreground image in the every frame video image before and after determine suppressing reach requirement, if the L frame all reaches requirement continuously, and repeated execution of steps B then; Described M, N and L are the positive integer greater than 1;
Wherein, whether exist shade to comprise in described every frame video image after determining respectively:
According to described track direction, determine moving target type to be detected, described moving target type comprises upstroke target and downward movement target; And for every frame video image X, carry out respectively following processing:
Detect the target area at the moving target place of each respective type in the lane detection zone from described video image X, described target area is the minimum rectangular area that comprises moving target;
Two sub regions about each target area is equally divided into are calculated the average gradient amplitude of every sub regions
With
And calculate
With
In smaller value and the ratio ρ of higher value;
Calculate the mean value of ρ corresponding to all target areas
With described
Compare with the first threshold that sets in advance, if described
Less than described first threshold, then determine to have shade among the described video image X;
Described according to the HSV colour model, every frame video image is afterwards carried out respectively shade suppress to comprise:
For the video image Y of every frame brightness, colourity yuv format, carry out respectively following processing:
Travel through each pixel among the video image Y of described yuv format, find out luminance component less than the pixel of the luminance component of the corresponding pixel points in the background image of the yuv format that obtains in advance, described corresponding pixel points is the identical pixel of coordinate; The video image Y of described yuv format and the background image of described yuv format all are converted to the HSV form, and three parameters are set;
Each pixel Z for finding out, carry out respectively following processing:
Calculate the luminance component of described pixel Z in the HSV model and the ratio of the luminance component of described corresponding pixel points in the HSV model, if this ratio is positioned at the interval that sets in advance, then the value of first parameter is set to 1, otherwise, be set to 0; Calculate the saturation degree component of described pixel Z in the HSV model and the difference of the saturation degree component of described corresponding pixel points in the HSV model, if this difference less than the Second Threshold that sets in advance, then the value of second parameter is set to 1, otherwise, be set to 0; Calculate the tone component of described pixel Z in the HSV model and the absolute value of the difference of the tone component of described corresponding pixel points in the HSV model, if this absolute value is less than the 3rd threshold value that sets in advance, then the value of the 3rd parameter is set to 1, otherwise, be set to 0;
Value with each parameter multiply by respectively the weights that set in advance for it, and with the multiplied result addition, if addition result, determines then that described pixel Z is shadow spots greater than the 4th threshold value that sets in advance, and with its filtering.
2. method according to claim 1 is characterized in that, and is described according to described track direction, determines that moving target type to be detected comprises:
If the regulation vehicle is kept to the right, then when described track direction be during from the lower left to the upper right side, described moving target type to be detected is the upstroke target, and when described track direction is during from the lower right to the upper left side, described moving target type to be detected is the downward movement target;
If the regulation vehicle is kept to the left, then when described track direction be during from the lower left to the upper right side, described moving target type to be detected is the downward movement target, and when described track direction is during from the lower right to the upper left side, described moving target type to be detected is the upstroke target.
3. method according to claim 1 is characterized in that, whether the registration of the foreground image in the every frame video image before and after described definite the inhibition reaches requirement comprises:
Calculate the registration of the foreground image in the every frame video image before and after suppressing, described registration and the 5th threshold value that sets in advance are compared, if described registration, determines then that described registration reaches requirement greater than described the 5th threshold value.
4. the video image processing device under the intelligent transportation monitoring scene is characterized in that, comprising:
Track orientation determination module is used for determining the lane detection zone, and determines the track direction by the running orbit of analyzing the moving target in the lane detection zone in the continuous N frame video image, and the track direction of determining is sent to the shadow Detection module;
Described shadow Detection module is used for according to described track direction, whether has shade in the every frame video image after determining respectively, all has shade in the N continuous frame video image if determine, and then notifies shade to suppress module and carries out self function;
Described shade suppresses module, be used for according to hue, saturation, intensity HSV colour model, every frame video image is afterwards carried out respectively shade to be suppressed, and whether the registration of the foreground image in the every frame video image before and after determining to suppress reaches requirement, if the L frame all reaches requirement continuously, then notify described shadow Detection module to repeat self function; Described M, N and L are the positive integer greater than 1;
Wherein, described shadow Detection module comprises:
The first processing unit is used for according to described track direction, determines moving target type to be detected, and described moving target type comprises upstroke target and downward movement target; And for every frame video image X, carry out respectively following processing: detect the target area at the moving target place of each respective type in the lane detection zone from described video image X, described target area is the minimum rectangular area that comprises moving target; Two sub regions about each target area is equally divided into are calculated the average gradient amplitude of every sub regions
With
And calculate
With
In smaller value and the ratio ρ of higher value; Calculate the mean value of ρ corresponding to all target areas
With described
Compare with the first threshold that sets in advance, if described
Less than described first threshold, then determine to have shade among the described video image X;
The second processing unit is used for notifying described shade to suppress module execution self function when described the first processing unit is determined the N continuous frame video image and all had shade;
Described shade suppresses module and comprises:
The 3rd processing unit, be used for the video image Y for every frame brightness, colourity yuv format, carry out respectively following processing: travel through each pixel among the video image Y of described yuv format, find out luminance component less than the pixel of the luminance component of the corresponding pixel points in the background image of the yuv format that obtains in advance, described corresponding pixel points is the identical pixel of coordinate; The video image Y of described yuv format and the background image of described yuv format all are converted to the HSV form, and three parameters are set; For each the pixel Z that finds out, carry out respectively following processing: calculate the luminance component of described pixel Z in the HSV model and the ratio of the luminance component of described corresponding pixel points in the HSV model, if this ratio is positioned at the interval that sets in advance, then the value of first parameter is set to 1, otherwise, be set to 0, calculate the saturation degree component of described pixel Z in the HSV model and the difference of the saturation degree component of described corresponding pixel points in the HSV model, if this difference is less than the Second Threshold that sets in advance, then the value of second parameter is set to 1, otherwise, be set to 0, calculate the tone component of described pixel Z in the HSV model and the absolute value of the difference of the tone component of described corresponding pixel points in the HSV model, if this absolute value is less than the 3rd threshold value that sets in advance, then the value of the 3rd parameter is set to 1, otherwise, be set to 0; Value with each parameter multiply by respectively the weights that set in advance for it, and with the multiplied result addition, if addition result, determines then that described pixel Z is shadow spots greater than the 4th threshold value that sets in advance, and with its filtering; Afterwards, calculate the registration of the foreground image in the every frame video image before and after suppressing, described registration and the 5th threshold value that sets in advance are compared, if described registration, determines then that described registration reaches requirement greater than described the 5th threshold value;
The manages the unit everywhere, is used for notifying described shadow Detection module to repeat self function when described the 3rd processing unit is determined continuous L frame video image and all reached requirement.
5. device according to claim 4 is characterized in that,
If the regulation vehicle is kept to the right, then when described track direction be during from the lower left to the upper right side, described moving target type to be detected is the upstroke target, and when described track direction is during from the lower right to the upper left side, described moving target type to be detected is the downward movement target;
If the regulation vehicle is kept to the left, then when described track direction be during from the lower left to the upper right side, described moving target type to be detected is the downward movement target, and when described track direction is during from the lower right to the upper left side, described moving target type to be detected is the upstroke target.
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| CN103632351B (en) * | 2013-12-16 | 2017-01-11 | 武汉大学 | All-weather traffic image enhancement method based on brightness datum drift |
| CN104156727B (en) * | 2014-08-26 | 2017-05-10 | 中电海康集团有限公司 | Lamplight inverted image detection method based on monocular vision |
| CN104899881B (en) * | 2015-05-28 | 2017-11-28 | 湖南大学 | Moving vehicle shadow detection method in a kind of video image |
| CN111488780A (en) * | 2019-08-30 | 2020-08-04 | 杭州海康威视系统技术有限公司 | Duration obtaining method and device, electronic equipment and storage medium |
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| EP1909230A1 (en) * | 2005-07-06 | 2008-04-09 | HONDA MOTOR CO., Ltd. | Vehicle and lane mark recognition apparatus |
| CN101236606A (en) * | 2008-03-07 | 2008-08-06 | 北京中星微电子有限公司 | Shadow cancelling method and system in vision frequency monitoring |
| CN101447082A (en) * | 2008-12-05 | 2009-06-03 | 华中科技大学 | Detection method of moving target on a real-time basis |
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| EP1909230A1 (en) * | 2005-07-06 | 2008-04-09 | HONDA MOTOR CO., Ltd. | Vehicle and lane mark recognition apparatus |
| CN101141633A (en) * | 2007-08-28 | 2008-03-12 | 湖南大学 | Moving object detecting and tracing method in complex scene |
| CN101236606A (en) * | 2008-03-07 | 2008-08-06 | 北京中星微电子有限公司 | Shadow cancelling method and system in vision frequency monitoring |
| CN101447082A (en) * | 2008-12-05 | 2009-06-03 | 华中科技大学 | Detection method of moving target on a real-time basis |
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